





"""simple docstring"""





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE__   =  16
SCREAMING_SNAKE_CASE__   =  32


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Accelerator				,		SCREAMING_SNAKE_CASE	:		int = 16     ):







      '''simple docstring'''




      lowerCAmelCase    		=			AutoTokenizer.from_pretrained("""bert-base-cased"""     )
      lowerCAmelCase    		=			load_dataset("""glue"""				,		"""mrpc"""     )

      def tokenize_function(SCREAMING_SNAKE_CASE	:		str     ):
            # max_length=None => use the model max length (it's actually the default)
            lowerCAmelCase    		=			tokenizer(examples["""sentence1"""]				,		examples["""sentence2"""]				,		truncation=SCREAMING_SNAKE_CASE				,		max_length=SCREAMING_SNAKE_CASE     )
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      # starting with the main process first:
      with accelerator.main_process_first():
            lowerCAmelCase    		=			datasets.map(
                SCREAMING_SNAKE_CASE				,		batched=SCREAMING_SNAKE_CASE				,		remove_columns=["""idx""", """sentence1""", """sentence2"""]				,		)

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      lowerCAmelCase    		=			tokenized_datasets.rename_column("""label"""				,		"""labels"""     )

      def collate_fn(SCREAMING_SNAKE_CASE	:		Tuple     ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            lowerCAmelCase    		=			1_28 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  lowerCAmelCase    		=			16
            elif accelerator.mixed_precision != "no":
                  lowerCAmelCase    		=			8
            else:
                  lowerCAmelCase    		=			None

            return tokenizer.pad(
                SCREAMING_SNAKE_CASE				,		padding="""longest"""				,		max_length=SCREAMING_SNAKE_CASE				,		pad_to_multiple_of=SCREAMING_SNAKE_CASE				,		return_tensors="""pt"""				,		)

      # Instantiate dataloaders.
      lowerCAmelCase    		=			DataLoader(
          tokenized_datasets["""train"""]				,		shuffle=SCREAMING_SNAKE_CASE				,		collate_fn=SCREAMING_SNAKE_CASE				,		batch_size=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			DataLoader(
          tokenized_datasets["""validation"""]				,		shuffle=SCREAMING_SNAKE_CASE				,		collate_fn=SCREAMING_SNAKE_CASE				,		batch_size=SCREAMING_SNAKE_CASE     )

      return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       SCREAMING_SNAKE_CASE__   =  mocked_dataloaders  # noqa: F811


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int				,		SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,		SCREAMING_SNAKE_CASE     ) == "1":
            lowerCAmelCase    		=			2
      # Initialize accelerator
      lowerCAmelCase    		=			Accelerator(cpu=args.cpu				,		mixed_precision=args.mixed_precision     )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCAmelCase    		=			config["""lr"""]
      lowerCAmelCase    		=			int(config["""num_epochs"""]     )
      lowerCAmelCase    		=			int(config["""seed"""]     )
      lowerCAmelCase    		=			int(config["""batch_size"""]     )

      lowerCAmelCase    		=			evaluate.load("""glue"""				,		"""mrpc"""     )

      # If the batch size is too big we use gradient accumulation
      lowerCAmelCase    		=			1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            lowerCAmelCase    		=			batch_size // MAX_GPU_BATCH_SIZE
            lowerCAmelCase    		=			MAX_GPU_BATCH_SIZE

      set_seed(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase		,						lowerCAmelCase    		=			get_dataloaders(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCAmelCase    		=			AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""				,		return_dict=SCREAMING_SNAKE_CASE     )

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      lowerCAmelCase    		=			model.to(accelerator.device     )

      # Instantiate optimizer
      lowerCAmelCase    		=			AdamW(params=model.parameters()				,		lr=SCREAMING_SNAKE_CASE     )

      # Instantiate scheduler
      lowerCAmelCase    		=			get_linear_schedule_with_warmup(
          optimizer=SCREAMING_SNAKE_CASE				,		num_warmup_steps=1_00				,		num_training_steps=(len(SCREAMING_SNAKE_CASE     ) * num_epochs) // gradient_accumulation_steps				,		)

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			accelerator.prepare(
          SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

      # Now we train the model
      for epoch in range(SCREAMING_SNAKE_CASE     ):
            model.train()
            for step, batch in enumerate(SCREAMING_SNAKE_CASE     ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device     )
                  lowerCAmelCase    		=			model(**SCREAMING_SNAKE_CASE     )
                  lowerCAmelCase    		=			outputs.loss
                  lowerCAmelCase    		=			loss / gradient_accumulation_steps
                  accelerator.backward(SCREAMING_SNAKE_CASE     )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            lowerCAmelCase    		=			0
            for step, batch in enumerate(SCREAMING_SNAKE_CASE     ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device     )
                  with torch.no_grad():
                        lowerCAmelCase    		=			model(**SCREAMING_SNAKE_CASE     )
                  lowerCAmelCase    		=			outputs.logits.argmax(dim=-1     )
                  lowerCAmelCase		,						lowerCAmelCase    		=			accelerator.gather((predictions, batch["""labels"""])     )
                  # New Code #
                  # First we check if it's a distributed system
                  if accelerator.use_distributed:
                        # Then see if we're on the last batch of our eval dataloader
                        if step == len(SCREAMING_SNAKE_CASE     ) - 1:
                              # Last batch needs to be truncated on distributed systems as it contains additional samples
                              lowerCAmelCase    		=			predictions[: len(eval_dataloader.dataset     ) - samples_seen]
                              lowerCAmelCase    		=			references[: len(eval_dataloader.dataset     ) - samples_seen]
                        else:
                              # Otherwise we add the number of samples seen
                              samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
                  metric.add_batch(
                      predictions=SCREAMING_SNAKE_CASE				,		references=SCREAMING_SNAKE_CASE				,		)

            lowerCAmelCase    		=			metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'epoch {epoch}:'				,		SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			argparse.ArgumentParser(description="""Simple example of training script."""     )
      parser.add_argument(
          """--mixed_precision"""				,		type=SCREAMING_SNAKE_CASE				,		default=SCREAMING_SNAKE_CASE				,		choices=["""no""", """fp16""", """bf16""", """fp8"""]				,		help="""Whether to use mixed precision. Choose"""
          """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
          """and an Nvidia Ampere GPU."""				,		)
      parser.add_argument("""--cpu"""				,		action="""store_true"""				,		help="""If passed, will train on the CPU."""     )
      lowerCAmelCase    		=			parser.parse_args()
      lowerCAmelCase    		=			{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       main()













"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'fnet'





      def __init__(			self ,		lowercase=32_000 ,		lowercase=768 ,		lowercase=12 ,		lowercase=3_072 ,		lowercase="gelu_new" ,		lowercase=0.1 ,		lowercase=512 ,		lowercase=4 ,		lowercase=0.02 ,		lowercase=1e-12 ,		lowercase=False ,		lowercase=512 ,		lowercase=3 ,		lowercase=1 ,		lowercase=2 ,		**lowercase ,		)						->	int:
            super().__init__(pad_token_id=lowercase ,		bos_token_id=lowercase ,		eos_token_id=lowercase ,		**lowercase					)

            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			max_position_embeddings
            lowerCAmelCase    		=			hidden_size
            lowerCAmelCase    		=			num_hidden_layers
            lowerCAmelCase    		=			intermediate_size
            lowerCAmelCase    		=			hidden_act
            lowerCAmelCase    		=			hidden_dropout_prob
            lowerCAmelCase    		=			initializer_range
            lowerCAmelCase    		=			type_vocab_size
            lowerCAmelCase    		=			layer_norm_eps
            lowerCAmelCase    		=			use_tpu_fourier_optimizations
            lowerCAmelCase    		=			tpu_short_seq_length






